Generalization Bounds Derived IPM-Based Regularization for Domain Adaptation.

Area of Science:

• Machine Learning
• Artificial Intelligence

Background:

• Domain adaptation is crucial for transfer learning, addressing challenges posed by domain gaps.
• Existing methods often struggle with generalizing across different data distributions.

Purpose of the Study:

• To propose a novel framework for domain adaptation that improves generalization ability.
• To introduce a new regularizer that accounts for generalization bounds.

Main Methods:

• Developed a domain adaptation framework integrating source and target data.
• Introduced a regularization term based on the integral probability metric (IPM) to bound testing error.
• Formulated empirical risk minimization as a convex optimization problem for effective solving.

Main Results:

• The proposed regularization term, using IPM, is classifier-independent.
• Empirical studies demonstrated the method's effectiveness on synthetic regression and real-world classification data.
• The framework successfully reduces the impact of domain gaps.

Conclusions:

• The novel domain adaptation framework effectively improves generalization by addressing domain gaps.
• The IPM-based regularizer offers a robust approach to bounding errors in transfer learning.
• The method is computationally efficient and applicable to various learning models.